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Table of contents (7 chapters)
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Front Matter
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Back Matter
Keywords
About this book
Anticipatory Learning Classifier Systems describes the state of the art of anticipatory learning classifier systems-adaptive rule learning systems that autonomously build anticipatory environmental models. An anticipatory model specifies all possible action-effects in an environment with respect to given situations. It can be used to simulate anticipatory adaptive behavior.
Anticipatory Learning Classifier Systems highlights how anticipations influence cognitive systems and illustrates the use of anticipations for (1) faster reactivity, (2) adaptive behavior beyond reinforcement learning, (3) attentional mechanisms, (4) simulation of other agents and (5) the implementation of a motivational module. The book focuses on a particular evolutionary model learning mechanism, a combination of a directed specializing mechanism and a genetic generalizing mechanism. Experiments show that anticipatory adaptive behavior can be simulated by exploiting the evolving anticipatory model for even faster model learning, planning applications, and adaptive behavior beyond reinforcement learning.
Anticipatory Learning Classifier Systems gives a detailed algorithmic description as well as a program documentation of a C++ implementation of the system.
Authors and Affiliations
Bibliographic Information
Book Title: Anticipatory Learning Classifier Systems
Authors: Martin V. Butz
Series Title: Genetic Algorithms and Evolutionary Computation
DOI: https://doi.org/10.1007/978-1-4615-0891-5
Publisher: Springer New York, NY
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eBook Packages: Springer Book Archive
Copyright Information: Springer Science+Business Media New York 2002
Hardcover ISBN: 978-0-7923-7630-9Published: 31 January 2002
Softcover ISBN: 978-1-4613-5290-7Published: 05 November 2012
eBook ISBN: 978-1-4615-0891-5Published: 06 December 2012
Series ISSN: 1568-2587
Edition Number: 1
Number of Pages: XXVIII, 172